Detecting abusive language online

My student, Hannah Leblanc, has just defended her thesis looking at predicting abusive language. The document is

https://qspace.library.queensu.ca/handle/1974/26252

Rather than treat this as an empirical problem — gather all the signal you can, select attributes using training data, and then build a predictor using those attributes — she started with models of what might drive abusive language. In particular, abuse may be associated with subjectivity (objective language is less likely to be abusive, even if it contains individual words that might look abusive) and with otherness (abuse often results from one group targeting another). She also looked at emotion and mood signals and their association with abuse.

All of the models perform almost perfectly at detecting non-abuse; they struggle more with detecting abuse. Some of this comes from mislabelling — documents that are marked as abusive but really aren’t; but much of the rest comes from missing signal — abusive words disguised so that they don’t match the words of a lexicon.

Overall the model achieves accuracy of 95% and F-score of 0.91.

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